Machine Learning Engineer Career Guide
Machine learning engineers design software, predictive models, programs, and algorithms that help machines identify patterns and take actions independently, without receiving direction to perform these specific tasks. In this way, machine learning engineers are tasked with creating and improving artificial intelligence.
Machine learning engineers often collaborate with other groups, including software engineering and data science teams, to deploy their programs and algorithms that help create everything from customized news feeds to self-driving cars.
If you enjoy scripting, programming, analytics, and algorithms, a career as a machine learning engineer might be for you.
The goal of a machine learning engineer is to create algorithms and models that use data sets to learn and perform on their own. This artificial model of human intelligence allows computers to predict future events based on past data and apply what they’ve learned to grow more intelligent over time, all on their own.
While all forms of engineering are focused on the construction of something, machine learning engineers build algorithms and data sets that help software learn, compared to constructing buildings and other structures.
Machine learning is a subset of artificial intelligence. Others who work closely with machine learning engineers include:
- Deep learning engineers — As a facet of machine learning, deep learning engineers create algorithms based on much larger datasets than machine learning engineers.
- Data scientists — These professionals use their technical skills to solve complex problems and help organizations make better objective decisions. Data science combines aspects of mathematics, computer science, modeling, statistics, and analytics.
- Computer scientists — Computer scientists maximize the transmission and transformation of data. This role often combines aspects of mathematics, physics, engineering, and design.
Typically, machine learning engineers need a bachelor’s degree in computer science or a similar field, along with related certifications. However, a master’s degree may be required for more senior roles.
Some of the day-to-day duties of a machine learning engineer include:
- Developing algorithms — Machine learning algorithms need to be capable of accurately analyzing large volumes of historical data to make future predictions.
- Evaluating machine learning processes — Ensuring that systems effectively automate predictive models and generate optimized and accurate information.
- Performing statistical analyses — Transforms unstructured data into useful information.
- Testing — Machine learning engineers need to test the software they develop to ensure that the artificial intelligence operates as intended.
- Identifying programming bugs — Finding and fixing complex algorithm problems can enhance the software’s accuracy and improve its learning capabilities.
- Documenting and consulting — You’ll need to document the machine learning processes and consult with management to refine them further.
- Continue training and development — As a machine learning engineer, you’ll need to remain aware of the latest trends and changes in the industry.
Depending on the employer, there aren’t any required certifications to work as a machine learning engineer.
With this said, there are several standard certifications needed to excel in a role as a machine learning engineer, many of which can be obtained from multiple sources. And depending on the source, many certifications last a lifetime.
Common options include:
- ITIL®** Foundation Certification
- CompTIA Project+
Machine learning engineers should have a bachelor’s degree — whether earned online or in-person — in computer science, software development, or information technology. These education options can help prepare you for a career in machine learning by providing the fundamental skills you’ll need to be successful. You will learn to round out your ability to design, develop, and optimize systems. You will also gain key computer programming, web application, and development skills. Overall a degree program will help you learn how to solve problems and find solutions that work.
Computer Science – B.S.
Problem solvers and math lovers needed! Your task: ...
Problem solvers and math lovers needed! Your...
Problem solvers and math lovers needed! Your task:
Lay the groundwork for the computing breakthroughs that will enable tomorrow's technologies.
- Time: 70% of graduates in similar programs finish within 23 months.
- Tuition and fees: $3,920 per 6-month term.
- Transfer: Your previous college coursework and existing certifications may waive course requirements, helping you finish even faster.
You'll have the opportunity to earn these certifications:
- CompTIA Project+
- Axelos ITIL Foundation
Professionals who need the skills a computer science degree provides include computer systems analysts, computer programmers, artificial intelligence specialists, software engineers, machine learning engineers, and more.
Software Development – B.S.
You're a creative and tech genius who wants the bigger...
You're a creative and tech genius who wants the...
You're a creative and tech genius who wants the bigger opportunities.
- Time: 67% of graduates finish within 30 months.
- Tuition and fees: $3,920 per 6-month term.
- Certifications: several CompTIA included in this program.
CompTIA certifications included:
- CompTIA A+
- CompTIA Project +
Software development is the second-highest rated specialty within IT. Secure your future with a degree and additional certifications.
No need to wait for spring or fall semester. It's back-to-school time at WGU year-round. Get started by talking to an Enrollment Counselor today, and you'll be on your way to realizing your dream of a bachelor's or master's degree—sooner than you might think!
Whether entry-level or advanced, some of the skills you’ll need to successfully fill your role as a machine learning engineer include:
- Coding and software skills — You’ll need to be proficient in coding languages like Python, Java, and R, along with modeling and data architecture skills, and machine learning frameworks such as TensorFlow or Keras.
- A robust knowledge base — This includes not just knowledge of machine learning frameworks, libraries, and data structures and modeling, but also mathematics, statistics, and algorithms.
- Analytical and problem-solving skills — A large part of working as a machine learning engineer involves analyzing and coming up with solutions to issues as they arise.
- Written and verbal communication and collaboration skills — As a machine learning engineer, you’ll frequently collaborate with other employees and departments, and you’ll need to communicate what you know.
- Advanced math skills — Including linear algebra, Bayesian statistics, and group theory.
If you have many of these skills under your belt, let’s take a look at how much money you might expect to earn.
How Much Does a Machine Learning Engineer Make?
Machine learning engineers earn an average salary of $114,000 annually. This range includes a low of $78,000 and a high of $150,000.
Variables that can impact how much you earn as a machine learning engineer include your level of education (as well as relevant certifications), how much experience you have in the field, and your location.
What is the Projected Job Growth?
The need for machine learning engineers is expected to increase by 31% through 2026 as more and more organizations implement artificial intelligence into their business models. Some of the factors driving this increase include developing new software for:
-Connected and self-driving cars
-Stock market and other financial algorithms
-The increased use of AI in healthcare
-The need for artificial intelligence in manufacturing and advertising
Where Do Machine Learning Engineers Work?
Machine learning engineers can work in a variety of environments, including:
-Transportation — Artificial intelligence is increasingly found in cars and other vehicles, especially self-driving and autonomous models
-Healthcare — Software has successfully been used to detect and diagnose certain diseases in healthcare systems
-Finance — Machine learning is often used to detect fraudulent transactions and predict stock values
-Manufacturing — From robot warehouse pickers to solving supply chain issues, machine learning is increasingly found in the manufacturing industry
-Advertising — Artificial intelligence is frequently used to gauge an advertising campaign’s effectiveness before releasing it to the public
Regardless of the industry, we’ll outline which skills you’ll need to thrive as a machine learning engineer.